Evidence map›Paper›PMID 36816118›Full record

ArticleFrontiers in neuroscience2023

At-home sleep monitoring using generic ear-EEG.

Yousef R Tabar, Kaare B Mikkelsen, Nelly Shenton, Simon L Kappel, Astrid R Bertelsen, Reza Nikbakht, Hans O Toft, Chris H Henriksen, Martin C Hemmsen, Mike L Rank and 2 more

Abstract read
In one paragraph

Article in Frontiers in neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

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  9. A deep learning-enabled smart garment for accurate and versatile monitoring of sleep conditions in daily life.Proceedings of the National Academy of Sciences of the United States of America · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Yousef R TabarDepartment of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark.
Kaare B MikkelsenDepartment of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark.
Nelly ShentonT&W Engineering A/S, Allerød, Denmark.
Simon L KappelDepartment of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark.
Astrid R BertelsenT&W Engineering A/S, Allerød, Denmark.
Reza NikbakhtT&W Engineering A/S, Allerød, Denmark.
Hans O ToftT&W Engineering A/S, Allerød, Denmark.
Chris H HenriksenT&W Engineering A/S, Allerød, Denmark.
Martin C HemmsenT&W Engineering A/S, Allerød, Denmark.
Mike L RankT&W Engineering A/S, Allerød, Denmark.
Marit OttoDepartment of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark.
Preben KidmoseDepartment of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: A device comprising two generic earpieces with embedded dry electrodes for ear-centered electroencephalography (ear-EEG) was developed. The objective was to provide ear-EEG based sleep monitoring to a wide range of the population without tailoring the device to the individual. Methods: To validate the device ten healthy subjects were recruited for a 12-night sleep study. The study was divided into two parts; part A comprised two nights with both ear-EEG and polysomnography (PSG), and part B comprised 10 nights using only ear-EEG. In addition to the electrophysiological measurements, subjects filled out a questionnaire after each night of sleep. Results: The subjects reported that the ear-EEG system was easy to use, and that the comfort was better in part B. The performance of the system was validated by comparing automatic sleep scoring based on ear-EEG with PSG-based sleep scoring performed by a professional trained sleep scorer. Cohen's kappa was used to assess the agreement between the manual and automatic sleep scorings, and the study showed an average kappa value of 0.71. The majority of the 20 recordings from part A yielded a kappa value above 0.7. The study was compared to a companioned study conducted with individualized earpieces. To compare the sleep across the two studies and two parts, 7 different sleeps metrics were calculated based on the automatic sleep scorings. The ear-EEG nights were validated through linear mixed model analysis in which the effects of equipment (individualized vs. generic earpieces), part (PSG and ear-EEG vs. only ear-EEG) and subject were investigated. We found that the subject effect was significant for all computed sleep metrics. Furthermore, the equipment did not show any statistical significant effect on any of the sleep metrics. Discussion: These results corroborate that generic ear-EEG is a promising alternative to the gold standard PSG for sleep stage monitoring. This will allow sleep stage monitoring to be performed in a less obtrusive way and over longer periods of time, thereby enabling diagnosis and treatment of diseases with associated sleep disorders.

Indexed as

ear-EEGelectroencephalographyhome recordinglong-term sleep monitoringsleep monitoring

Identifiers

PMID36816118
PMCPMC9928964

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.